The　integration　of　Connected　Cruise　Control　(CCC)　and　wireless　Vehicle-to-Vehicle　(V2V)　communication　technology　aims　to　improve　driving　safety　and　stability.　To　enhance　CCC＇s　adaptability　in　complex　traffic　conditions,　in-depth　research　into　intelligent　asymmetrical　control　design　is　crucial.　In　this　paper,　the　intelligent　CCC　controller　issue　is　investigated　by　jointly　considering　the　dynamic　network-induced　delays　and　target　vehicle　speeds.　In　particular,　a　deep　reinforcement　learning　(DRL)-based　controller　design　method　is　introduced　utilizing　the　Twin　Delayed　Deep　Deterministic　Policy　Gradient　(TD3)　algorithm.　In　order　to　generate　intelligent　asymmetrical　control　strategies,　the　quadratic　reward　function,　determined　by　control　inputs　and　vehicle　state　errors　acquired　through　interaction　with　the　traffic　environment,　is　maximized　by　the　training　that　involves　both　actor　and　critic　networks.　In　order　to　counteract　performance　degradation　due　to　dynamic　platoon　factors,　the　impact　of　dynamic　target　vehicle　speeds　and　previous　control　strategies　is　incorporated　into　the　definitions　of　Markov　Decision　Process　(MDP),　CCC　problem　formulation,　and　vehicle　dynamics　analysis.　Simulation　results　show　that　our　proposed　intelligent　asymmetrical　control　algorithm　is　well-suited　for　dynamic　traffic　scenarios　with　network-induced　delays　and　outperforms　existing　methods.